Current screening methods for factor VIII gene (F8) mutations can reveal the causative alteration in the vast majority of haemophilia A patients. Yet, standard diagnostic methods fail in about 2 % of cases. This study aimed at analysing the entire intronic sequences of the F8 gene in 15 haemophilia A patients by next generation sequencing. All patients had a mild to moderate phenotype and no mutation in the coding sequence and splice sites of the F8 gene could be diagnosed so far. Next generation sequencing data revealed 23 deep intronic candidate variants in several F8 introns, including six recurrent variants and three variants that have been described before. One patient additionally showed a deletion of 9.2 kb in intron 1, mediated by Alu-type repeats. Several bioinformatic tools were used to score the variants in comparison to known pathogenic F8 mutations in order to predict their deleteriousness. Pedigree analyses showed a correct segregation pattern for three of the presumptive mutations. In each of the 15 patients analysed, at least one deep intronic variant in the F8 gene was identified and predicted to alter F8 mRNA splicing. Reduced F8 mRNA levels and/or stability would be well compatible with the patients’ mild to moderate haemophilia A phenotypes. The next generation sequencing approach used proved an efficient method to screen the complete F8 gene and could be applied as a one-stop sequencing method for molecular diagnostics of haemophilia A. Note: The work was carried out at the Department of Human Genetics, University of Würzburg, Biocenter, Am Hubland, 97074 Würzburg, Germany. New

Lung cancer is currently the leading cause of cancer related mortality due to late diagnosis and limited treatment intervention. Non-coding RNAs are not translated into proteins and have emerged as fundamental regulators of gene expression. Recent studies reported that microRNAs and long non-coding RNAs are involved in lung cancer development and progression. Moreover, they appear as new promising non-invasive biomarkers for early lung cancer diagnosis. Here, we highlight their potential as biomarker in lung cancer and present how bioinformatics can contribute to the development of non-invasive diagnostic tools. For this, we discuss several bioinformatics algorithms and software tools for a comprehensive understanding and functional characterization of microRNAs and long non-coding RNAs.

To evaluate the role of constitutive epigenetic changes in normal body cells of BRCA1/BRCA2‐mutation negative patients, we have developed a deep bisulfite sequencing assay targeting the promoter regions of 8 tumor suppressor (TS) genes (BRCA1, BRCA2, RAD51C, ATM, PTEN, TP53, MLH1, RB1) and the estrogene receptor gene (ESR1), which plays a role in tumor progression. We analyzed blood samples of two breast cancer (BC) cohorts with early onset (EO) and high risk (HR) for a heterozygous mutation, respectively, along with age‐matched controls. Methylation analysis of up to 50,000 individual DNA molecules per gene and sample allowed quantification of epimutations (alleles with >50% methylated CpGs), which are associated with epigenetic silencing. Compared to ESR1, which is representative for an average promoter, TS genes were characterized by a very low (< 1%) average methylation level and a very low mean epimutation rate (EMR; < 0.0001% to 0.1%). With exception of BRCA1, which showed an increased EMR in BC (0.31% vs. 0.06%), there was no significant difference between patients and controls. One of 36 HR BC patients exhibited a dramatically increased EMR (14.7%) in BRCA1, consistent with a disease‐causing epimutation. Approximately one third (15 of 44) EO BC patients exhibited increased rates of single CpG methylation errors in multiple TS genes. Both EO and HR BC patients exhibited global underexpression of blood TS genes. We propose that epigenetic abnormalities in normal body cells are indicative of disturbed mechanisms for maintaining low methylation and appropriate expression levels and may be associated with an increased BC risk.

Over recent years next generation sequencing (NGS) technologies evolved from costly tools used by very few, to a much more accessible and economically viable technology. Through this recently gained popularity, its use-cases expanded from research environments into clinical settings. But the technical know-how and infrastructure required to analyze the data remain an obstacle for a wider adoption of this technology, especially in smaller laboratories. We present GensearchNGS, a commercial DNAseq software suite distributed by Phenosystems SA. The focus of GensearchNGS is the optimal usage of already existing infrastructure, while keeping its use simple. This is achieved through the integration of existing tools in a comprehensive software environment, as well as custom algorithms developed with the restrictions of limited infrastructures in mind. This includes the possibility to connect multiple computers to speed up computing intensive parts of the analysis such as sequence alignments. We present a typical DNAseq workflow for NGS data analysis and the approach GensearchNGS takes to implement it. The presented workflow goes from raw data quality control to the final variant report. This includes features such as gene panels and the integration of online databases, like Ensembl for annotations or Cafe Variome for variant sharing.

Allele frequencies vary across populations and loci, even in the presence of migration. While most differences may be due to genetic drift, divergent selection will further increase differentiation at some loci. Identifying those is key in studying local adaptation, but remains statistically challenging. A particularly elegant way to describe allele frequency differences among populations connected by migration is the F-model, which measures differences in allele frequencies by population specific F_ST coefficients. This model readily accounts for multiple evolutionary forces by partitioning F_ST coefficients into locus and population specific components reflecting selection and drift, respectively. Here we present an extension of this model to linked loci by means of a hidden Markov model (HMM), which characterizes the effect of selection on linked markers through correlations in the locus specific component along the genome. Using extensive simulations we show that the statistical power of our method is up to two-fold that of previous implementations that assume sites to be independent. We finally evidence selection in the human genome by applying our method to data from the Human Genome Diversity Project (HGDP).

Dystrophinopathies are the most common muscle diseases, especially in men. In women, on the other hand, a manifestation of Duchenne muscular dystrophy is rare due to X-chromosomal inheritance. We present two young girls with severe muscle weakness, muscular dystrophies, and creatine kinase (CK) levels exceeding 10,000 U/L. In the skeletal muscle tissues, dystrophin staining reaction showed mosaicism. The almost entirely skewed X-inactivation in both cases supported the possibility of a dystrophinopathy. Despite standard molecular diagnostics (including multiplex ligation-dependent probe amplification (MLPA) and next generation sequencing (NGS) gene panel sequencing), the genetic cause of the girls’ conditions remained unknown. However, whole-genome sequencing revealed two reciprocal translocations between their X chromosomes and chromosome 5 and chromosome 19, respectively. In both cases, the breakpoints on the X chromosomes were located directly within the DMD gene (in introns 54 and 7, respectively) and were responsible for the patients’ phenotypes. Additional techniques such as Sanger sequencing, conventional karyotyping and fluorescence in situ hybridization (FISH) confirmed the disruption of DMD gene in both patients through translocations. These findings underscore the importance of accurate clinical data combined with histopathological analysis in pinpointing the suspected underlying genetic disorder. Moreover, our study illustrates the viability of whole-genome sequencing as a time-saving and highly effective method for identifying genetic factors responsible for complex genetic constellations in Duchenne muscular dystrophy (DMD).